Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading. (arXiv:2109.06736v1 [cs.IR])
(2 min)
Sequential modelling entails making sense of sequential data, which naturally
occurs in a wide array of domains. One example is systems that interact with
users, log user actions and behaviour, and make recommendations of items of
potential interest to users on the basis of their previous interactions. In
such cases, the sequential order of user interactions is often indicative of
what the user is interested in next. Similarly, for systems that automatically
infer the semantics of text, capturing the sequential order of words in a
sentence is essential, as even a slight re-ordering could significantly alter
its original meaning. This thesis makes methodological contributions and new
investigations of sequential modelling for the specific application areas of
systems that recommend music tracks to listeners and systems that process text
semantics in order to automatically fact-check claims, or "speed read" text for
efficient further classification. (Rest of abstract omitted due to arXiv
abstract limit)